Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps
The tooth-marked tongue is an important indicator in traditional Chinese medicinal diagnosis. However, the clinical competence of tongue diagnosis is determined by the experience and knowledge of the practitioners. Due to the characteristics of different tongues, having many variations such as diffe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/11/2/45 |
| _version_ | 1819229977944522752 |
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| author | Yue Sun Songmin Dai Jide Li Yin Zhang Xiaoqiang Li |
| author_facet | Yue Sun Songmin Dai Jide Li Yin Zhang Xiaoqiang Li |
| author_sort | Yue Sun |
| collection | DOAJ |
| description | The tooth-marked tongue is an important indicator in traditional Chinese medicinal diagnosis. However, the clinical competence of tongue diagnosis is determined by the experience and knowledge of the practitioners. Due to the characteristics of different tongues, having many variations such as different colors and shapes, tooth-marked tongue recognition is challenging. Most existing methods focus on partial concave features and use specific threshold values to classify the tooth-marked tongue. They lose the overall tongue information and lack the ability to be generalized and interpretable. In this paper, we try to solve these problems by proposing a visual explanation method which takes the entire tongue image as an input and uses a convolutional neural network to extract features (instead of setting a fixed threshold artificially) then classifies the tongue and produces a coarse localization map highlighting tooth-marked regions using Gradient-weighted Class Activation Mapping. Experimental results demonstrate the effectiveness of the proposed method. |
| first_indexed | 2024-12-23T11:21:45Z |
| format | Article |
| id | doaj.art-7c7a5188bfd147f497bc1e1df9a14107 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-12-23T11:21:45Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-7c7a5188bfd147f497bc1e1df9a141072022-12-21T17:49:04ZengMDPI AGFuture Internet1999-59032019-02-011124510.3390/fi11020045fi11020045Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation MapsYue Sun0Songmin Dai1Jide Li2Yin Zhang3Xiaoqiang Li4School of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaThe tooth-marked tongue is an important indicator in traditional Chinese medicinal diagnosis. However, the clinical competence of tongue diagnosis is determined by the experience and knowledge of the practitioners. Due to the characteristics of different tongues, having many variations such as different colors and shapes, tooth-marked tongue recognition is challenging. Most existing methods focus on partial concave features and use specific threshold values to classify the tooth-marked tongue. They lose the overall tongue information and lack the ability to be generalized and interpretable. In this paper, we try to solve these problems by proposing a visual explanation method which takes the entire tongue image as an input and uses a convolutional neural network to extract features (instead of setting a fixed threshold artificially) then classifies the tongue and produces a coarse localization map highlighting tooth-marked regions using Gradient-weighted Class Activation Mapping. Experimental results demonstrate the effectiveness of the proposed method.https://www.mdpi.com/1999-5903/11/2/45tooth-marked tongueconvolutional neural networkgradient-weighted class activation maps |
| spellingShingle | Yue Sun Songmin Dai Jide Li Yin Zhang Xiaoqiang Li Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps Future Internet tooth-marked tongue convolutional neural network gradient-weighted class activation maps |
| title | Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps |
| title_full | Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps |
| title_fullStr | Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps |
| title_full_unstemmed | Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps |
| title_short | Tooth-Marked Tongue Recognition Using Gradient-Weighted Class Activation Maps |
| title_sort | tooth marked tongue recognition using gradient weighted class activation maps |
| topic | tooth-marked tongue convolutional neural network gradient-weighted class activation maps |
| url | https://www.mdpi.com/1999-5903/11/2/45 |
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